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The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

According to International Data Corporation (IDC), stored data is set to increase by 250% by 2025 , with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.

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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

The data universe is expected to grow exponentially with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase pressure to manage cloud costs efficiently and complicate governance of AI and data workloads.

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How to Build ETL Data Pipeline in ML

The MLOps Blog

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. What is an ETL data pipeline in ML?

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning Blog

Duration of data informs on long-term variations and patterns in the dataset that would otherwise go undetected and lead to biased and ill-informed predictions. Breaking down these data silos to unite the untapped potential of the scattered data can save and transform many lives. Much of this work comes down to the data.”

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Using Snowflake Data as an Insurance Company

phData

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.